Contract lifecycle management with AI: PwC

Key takeaways:

Contracting is shifting from manual workflows to intelligent orchestration
AI agents can help legal and business teams focus on higher-value work
Consider a three-phased progression from foundational CLM to orchestrated, agent-enabled contracting

Contract lifecycle management (CLM) solutions have long signaled the promise of intelligent contracting — but that promise remains only partially realized. The idea is compelling: business decisions informed by insights from past contracting activities, risk addressed through structured redlining aligned to approved playbooks, and revenue protected through proactive monitoring of contractual obligations. More often than not, in our experience, many organizations lack clarity on end-to-end contracting ownership, contributing to loss of contract value and missed obligations. At the same time, legal teams are under pressure - managing growing workloads with limited capacity, often reacting to risk rather than anticipating it.

CLM technology has evolved significantly over the past two decades. What once relied on emails, spreadsheets, and manual tracking has shifted to centralized repositories, template-driven authoring, and workflow-based approvals. Contracts can now be generated from pre-approved clause libraries, routed to relevant stakeholders, and tracked through automated notifications. These advances have helped streamline processes, but they haven’t fundamentally changed how contracting operates.

The underlying model remains largely human-led. Ownership is fragmented across legal, procurement, sales, finance, and IT. Data can be inconsistent. Approvals are delayed. Obligation tracking is often manual. And despite continued investment, adoption challenges persist—driven in part by systems that require users to adapt to rigid configurations rather than aligning to how work actually gets done. The result is rework, low utilization, and repeated cycles of replatforming.

The path forward isn’t just about improving systems. It’s about rethinking the contracting model. AI agents are already transforming the procurement process. Contracting can be the logical next step. AI-enabled contracting, enabled by agentic AI, introduces an opportunity to reshape how contracts are created, managed, and acted on—so organizations can move from reactive processes to more connected, insight-driven operations.

How contracting is evolving in the agentic era

Imagine a frictionless contracting process that values your time - the person who needs to get contracts out quickly and reliably, and move on to sign the next deal. Picture a future where your day is spent making decisions, rather than in endless admin tasks, grappling with technicalities. We’re rapidly approaching a reality where contracting technology isn’t bureaucracy you fight but an assistant that helps you at every step - getting things ready for you, performing due diligence, and collating the details that matter - so you can make informed, insight-driven decisions.

Agentic CLM represents that shift - moving contracting beyond streamlined workflows toward an exception-driven model, where AI facilitates routine execution and escalates decisions that require human expertise.

Industry leading corporations are already adopting agentic AI at a rapid pace. Sixty-six percent of agentic-enabled respondents reported measurable productivity improvements in our PwC AI Agent Survey 2025. As competitors across industries enable agentic AI, they’re beginning to close deals quicker, with less risk and more lean processes, and with better revenue and expense management. Agentic AI is rapidly becoming the business standard. With contracting identified as a key target area for AI implementation, companies that do not invest now will likely fall behind the curve.

Agentic AI introduces autonomous, goal-oriented systems that can reason, plan, and act within defined guardrails. Rather than waiting for human prompts, agents operate continuously to coordinate work across CLM, customer relationship management (CRM), sourcing, enterprise resource planning (ERP), and adjacent systems, escalating to a human only when judgment is required.

Agents are steadily finding their way into CLM platforms—starting with drafting, redlining, and compliance monitoring agents, but with a broader focus to cover all aspects of the contracting lifecycle. Together, these agents can help transform contracting from a document-centric workflow into a continuously managed system.

Effectively deploying agentic AI for contracting processes

Successfully deploying an agentic solution requires not just changing your technology, but also your operating model. Tightly coupled manual processes and corresponding behaviors that have ossified over time should be decoupled to get more out of agentic systems.

Successful implementation involves preparing your organization, stabilizing your data and processes, and progressively embedding autonomy into daily operations. Industry leading organizations typically follow a structured, phased approach to move from early readiness to full orchestration.

Before introducing agents, identify the business units, workflows, and processes that will likely be impacted. This includes clarifying ownership across legal, procurement, sales, risk, and operations teams, and aligning stakeholders on where autonomy can add value.

At the same time, contract data and content should be prepared for intelligent use: Repositories should be assessed, key contract types and templates identified, and unstructured agreements organized to help digitization and analysis. Establishing this foundation allows you to determine where agentic capabilities can be introduced responsibly and effectively.

Investing in this preparation stage helps you enter the transformation with clear scope, aligned stakeholders, and data that's ready to support more advanced automation. Once this preliminary work is completed, we follow three phases of implementation for agentic AI: digitize and prepare, integrate and learn, and full orchestration.

The objective of this phase is to create a contracting environment that is reliable, governable, and ready to support autonomous execution. In practice, this means unstructured contracts are digitized, repositories are consolidated, and structured data is cleaned so that the organization has a consistent view of its contractual obligations and risks. Early, low-risk agents can classify documents, extract key metadata, identify gaps or duplicates across repositories, and surface inconsistencies in workflows or data structures. Organizations that succeed in this phase can emerge with both cleaner data and stronger governance maturity, along with clarity of ownership, confidence in their data, and a stable operating model that can scale. This phase creates the conditions required for more advanced agentic behavior.

Once the compliance landscape is identified and the data is stabilized, agents can start to be integrated into daily practice to help the contracting process, while teams learn how to supervise, escalate, and refine these new collaborators. Data governance models should be used throughout this phase to enable continued progress towards full agentic transformation of the contracting process.

Modern software-as-a-service CLM platforms play an important role in this phase, providing the structured data, standardized workflows, and API connectivity that agents rely on to operate effectively. Clear escalation paths and consistent feedback loops are essential to keeping agent behavior consistent and accurate. Many organizations that skip this learning period often struggle with adoption because their workforce is unprepared to oversee and guide increasingly autonomous systems. To help mitigate this, key stakeholders should continue to interact with agents, to become familiar with their capabilities and explore use cases that can propel the business into full orchestration.

This is where agentic CLM becomes truly transformational. Agents are developed to operate across CLM, CRM, ERP, and adjacent systems -proactively monitoring obligations in real time, initiating tasks before bottlenecks appear, assembling or revising documents based on established patterns, and escalating issues to humans only when a decision requires judgment or contextual insight.

Changing your ways of working with an agentic CLM

Human involvement is shifting from manual reviews to exception handling, risk judgment, and overall governance of the contracting function.

These changes extend across your organization. Legal professionals can focus more on policy, governance, and complex matters, rather than mechanical document review. Procurement teams can devote more time to supplier strategy, performance management, and commercial outcomes. Sales and commercial functions can prioritize relationships and deal velocity, instead of administrative contract tasks. People take on more judgment-driven responsibilities, reinforcing the importance of human insight even as agents assume a larger share of execution. Full orchestration doesn’t reduce the role of humans; it helps elevate their role by enabling teams to concentrate on the decisions and interactions that create more value.

Implementing an agent-first model alongside structured change management can help drive meaningful operational impact. These gains allow employees to focus on higher-value, strategic work while continuously refining and expanding agent-enabled capabilities. Agentic AI isn’t just an enhancement to CLM, but a strategic change in how contracting operates. Industry leaders in the market are leaning into these capabilities quickly and, as adoption accelerates, competitive advantages will increasingly favor early movers.